Semi-supervised hyperspectral image segmentation

• Published in: 2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing pp. 1 - 4
• Main Authors: Jun Li, Bioucas-Dias, J.M., Plaza, A.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.08.2009
• Subjects: Computational modeling; Hyperspectral image segmentation; Hyperspectral imaging; Hyperspectral sensors; Image classification; Image segmentation; Logistics; Markov random field; Markov random fields; Remote sensing; semisupervised classification; Semisupervised learning; Telecommunications
• ISBN: 9781424446865; 1424446864
• ISSN: 2158-6268
• DOI: 10.1109/WHISPERS.2009.5289082

This paper presents a new semi-supervised segmentation algorithm, suited to high dimensional data, of which hyperspectral images are an example. The algorithm implements two main steps: (a) semi-supervised learning, used to infer the class distributions, followed by (b) segmentation, by inferring the labels from a posterior density built on the learned class distributions and on a Markov random field. The class distributions are modeled with a multinomial logistic regression, where the regressors are learned using both labeled and, through a graph-based technique, unlabeled samples. The prior on the labels is a multi-level logistic model. The maximum a posterior segmentation is computed by the alpha-Expansion min-cut based integer optimization algorithm. We give experimental evidence that the spatial prior greatly improves the segmentation performance, with respect to that of a semi-supervised classifier. The effectiveness of the proposed method is demonstrated with simulated and real data.